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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT016F valid from Autumn 2019

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General
  • English
  • If sufficient demand
Aim
  • The goal of the course is
    to give students the tools and training to recognize convex optimization problems that arise in applications

    to present the basic theory of such problems, concentrating on results that are useful in computation

    to give students a thorough understanding of how such problems are solved, and some experience in solving them

    to give students the background required to use the methods in their own research work or applications
Contents
  • The course has three parts
    * Basic theory for convex sets and functions
    * Experience of formulation of application problems as convex optimization problems
    * Knowledge and experience of efficient optimization algorithms
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • * have knowledge about theory for convex sets and functions
    * understand how application problems can be formulated as convex optimization problems
    * have demonstrated understanding of how efficient algorithms are implemented and works
Competences and Skills
  • For a passing grade the doctoral student must
  • * have demonstrated skills in calculations with convex functions
    * be able to reformulate practical application problems as convex optimization problems
    * demonstrated ability in handling some existing program package for convex optimization and ability in writing code for simpler algorithms
Judgement and Approach
  • For a passing grade the doctoral student must
  • demonstrate the ability to critically evaluate and compare different formulations of convex optimization problems and different algorithms for different quality criteria
Types of Instruction
  • Lectures
  • Exercises
Examination Formats
  • Written exam
  • Written assignments
  • Miscellaneous
  • Weekly handin problems
    Take-home exam
    Students should take an active role in the weekly exercise sessions
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Linera algebra, calculus in several variables, probability theory
Selection Criteria
Literature
  • Boyd, S. & Vandenberghe, L.: Convex Optimization. Cambridge University Press.
  • Freely available on http://www.stanford.edu/~boyd/cvxbook/
Further Information
  • Replaces FRT015F.
Course code
  • FRT016F
Administrative Information
  •  -03-03
  • FN1/Anders Gustafsson

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